Sparse Capsule Networks. A naive solution to reduce uncertainty within the routing algorithm is to simply running more iterations. As shown by Paik et al. [1]} and Gu et al. [2]}, the routing algorithms tends to overly polarize the link strengths, namely a simple route in which each input capsule sends its output to only one capsule and all other routes are suppressed. On the one hand, this behavior is desirable because it makes the routing algorithm more explainable, by making it possible to extract a parse tree thanks to this coupling coefficients. On the other hand, running many iterations is only useful in the case of networks with few parameters, as demonstrated by Renzulli et al. [3]}, otherwise the performance will drop. Rawlinson  et al. [4]} trained CapsNets in an unsupervised setting, showing that in unsupervised learning the routing algorithm does not discriminate among capsule anymore: the coupling coefficients collapse to the same value. Therefore, they sparsify latent capsule layers activities by masking output capsules according to a custom ranking function.
Kosiorek et al. [5]} impose sparsity and entropy constraints into capsules, but they do not employ an iterative routing mechanism. Jeong et al. [6]} introduced a structured pruning layer called ladder capsule layers, which removes irrelevant capsules, namely capsules with low activities. Kakillioglu et al. [7]} solve the task of 3D object classification on point clouds with pruned Capsule Networks. Their objective was to compress robust capsule models in order to deploy them on resource-constrained devices.
